Do inputs matter?: using data-dependence profiling to evaluate thread level speculation in BG/Q
Bibliographic record
Abstract
Figure 1 shows the performance of three parallel versions (auto-SIMDized, auto-SIMDized+auto-OpenMP by bgxlc r and auto-SIMDized+auto-OpenMP+speculatively parallelized by an automatic speculative parallelization framework developed) of the SPEC2006 and PolyBench/C benchmarks. The speculative loops in lbm have 98% coverage that accounts for the speedup while in bzip2(35%) and dynprog (26%), the poor coverage of speculative loops introduces overhead. h264ref has the highest number of loops speculatively parallelized (47) but most of them have function calls that introduce dependences, thus causing slowdown (only 12% of speculative threads successfully committed). Filtering speculative execution of loops with non-side-effect-free function calls tackles the mispeculation overhead. cholesky and dynprog experience L1 cache misses due to LR mode(12% and 10% respectively) while jacobi and seidel experience huge dynamic path length increase (112% and 123% respectively over sequential).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".